Cascaded boosted predictive models

Abstract

Cascaded, boosted predictive models trained using distinct sets of exogenous and endogenous features are configured to predict component of performance ratings of entities. From the distinct predicted components, the second entity's rating factor can be determined. A second entity's rating factor represents the specific contribution a second entity makes to his average performance rating, as distinct from the rating that an arbitrary or hypothetical second entity would obtain.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of U.S. Provisional Application No. 62 / 307,368, filed Mar. 11, 2016, which is hereby incorporated by reference in its entirety.TECHNICAL FIELD[0002]This disclosure generally relates to the field of statistical machine learning in which systems have the capability to automatically update a current set of data and relationships using data mining, and more particularly to applications of boosting classifiers.[0003]Fields of Classification: Artificial Intelligence (Class 706), subclass 12 (Machine Learning); 707 (Data Processing), subclasses 776 (Data Mining); Cross Reference Art Collection: 905, 913 (Vehicles).BACKGROUND[0004]Statistical machine learning is a class of computer-implemented data analysis algorithms that identify relationships between variables (equivalently features or attributes) in complex data sets, for applications in classification, clustering, regression (prediction), ranking, and othe...

AI Technical Summary

Benefits of technology

The present patent system uses a unique approach compared to conventional gradient boosting methods by training models on different categories of features that are distinct from the specific features used in the other models. This results in better feature and model decorrelation with respect to conventional methods, leading to improved explanatory power in identifying the most significant variables in each feature category. The system also uses the ROAR factor to select drivers for providing a trip request, providing messages to drivers to improve their performance, and identifying drivers who may require additional training to improve their performance in the future.

Problems solved by technology

For example, existing methods do not inherently take into account whether an independent feature occurs before, during, or after an event or value being classified or predicted, whether a feature is an aspect of the environment itself or the result of an actor or agent in that environment.

Similarly, existing methods do not distinguish between features that are spontaneously observed and those that are intentionally elicited, or between features that assumed and those that are experimental.

These limitations result in limited ability of existing approaches to use these various types of contextual features of the data to improve the accuracy of the model.

Because such a rating reflects the rider's overall experience, it may, in fact, be based upon factors that are beyond the control of the driver—for example, a rider may provide a poor rating to the driver for a trip made during rush hour since the rider was delayed in reaching their destination, even though the rush hour traffic was beyond the control of the driver.

Because there are numerous factors that can contribute to the rating given by the rider, conventional approaches that strictly look at a driver's average performance rating do not accurately assess the driver's actual contribution to the performance rating.

As such, existing ensemble learning methods do not immediately lend themselves to applications for evaluating driver performance in transportation management systems.

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